54 th Natonal Conference of the Australan Agrcultural and Resource Economcs Socety Adelade, Australa 10 12 February 2010 TECHNICAL EFFICIENCY AND TECHNOLOGY GAPS ON CLEAN AND SAFE VEGETABLE FARMS IN NORTHERN THAILAND: A COMPARISON OF DIFFERENT TECHNOLOGIES Prathanthp Kramol 1, Renato Vllano 2, Euan Flemng 3 and Paul Krstansen 4 1. PhD student, School of Busness, Economcs and Publc Polcy, UNE; Researcher, Multple Croppng Centre, Faculty of Agrculture, Chang Ma Unversty, Thaland 2. Senor Lecturer, School of Busness, Economcs and Publc Polcy, UNE 3. Professor, School of Busness, Economcs and Publc Polcy, UNE 4. Lecturer, School of Envronmental and Rural Scence, UNE Correspondng author: Prathanthp Kramol School of Busness, Economcs and Publc Polcy Unversty of New England Armdale NSW 2351 Emal: pramol2@une.edu.au 1
Techncal effcency and technology gaps on clean and safe vegetable farms n northern Thaland: a comparson of dfferent technologes Prathanthp Kramol 1, Renato Vllano 2, Euan Flemng 3 and Paul Krstansen 4 Abstract Clean and safe agrcultural products are an mportant ssue among consumers, farmers and governments. Many developng countres develop ther produce at varous ponts along the clean contnuum based on producton practces related to use of synthetc chemcals. Organc farmng s appled to technologes wth no chemcals or synthetc fertlsers used durng producton or processng. It was ntally developed by farmers and non-government organsatons n Thaland, and subsequently mplemented by the Tha government through a seres of polces on clean produce to meet nternatonal standards. Safe-use and pestcdefree practces le between organc and conventonal practces, and are possble steps when convertng conventonal farms to organc farms. We compare the techncal effcences and technology gaps of the four farmng systems n northern Thaland of whch three - organc, pestcde-free and safe-use - are desgnated clean and safe. Farm-level data on vegetable producton were collected from random samples of farms usng these technologes. A metafronter model was estmated, enablng the estmaton of techncal effcences and technology gap ratos (TGRs) for vegetable farms operatng under the dfferent producton systems. Conventonal farms were expected to have the hghest mean TGR (smallest dstance from the metafronter) as they are least constraned n the way they farm, and results bear out ths expectaton. The mean TGR for conventonal farms s 0.80, sgnfcantly hgher than that for organc farms at 0.45. But all producton systems have farms lyng on the metafronter. In contrast to the TGR results, conventonal farms have the lowest mean techncal effcency relatve to ther group fronter (0.33) and pestcde-free vegetable farms the hghest (0.47), most lely reflectng the dfferent degrees of techncal assstance provded to farmers n these groups. Organc farmng s that farmers n ths group dd not perform maredly worse than farmers n other groups n terms of productvty. There are numerous organsatons and projects provdng assstance for clean and safe vegetable farmng n northern Thaland. Scope exsts to mprove the performance of farmers n all groups as techncal effcences and TGRs of farms vary wdely n all groups. Improvements are needed for agronomc technology, supply chans, farmer capacty n producton and maretng, and effectveness of technology transfer strateges. Key words: organc, techncal effcency, stochastc fronter, metafronter, northern Thaland 1 2 3 4 PhD student, School of Busness, Economcs and Publc Polcy, UNE; Researcher, Multple Croppng Centre, Faculty of Agrculture, Chang Ma Unversty, Thaland Senor Lecturer, School of Busness, Economcs and Publc Polcy, UNE Professor, School of Busness, Economcs and Publc Polcy, UNE Lecturer, School of Envronmental and Rural Scence, UNE 2
1. Introducton The effect of agrcultural produce on safety, health and the envronmental has ganed ncreasng attenton to consumers across the globe. Whle developed countres nterests are prmarly focused on certfed organc producton, Thaland, as a developng country, has adopted varous levels of clean such as safe-use chemcal, pestcde-free and no chemcal to envronmental frendly practces notably as organc. Clean and safe maret demand contnues to ncrease (Vant-Anuncha and Schmdt 2005; Posr et al. 2007; Johnson et al. 2008), but lac of clean and safe produce of good qualty and n suffcent varety are constranng development of the clean and safe vegetable ndustry (Kramol et al. 2005; Posr et al. 2007; Lorlowhaarn et al. 2008). In ths paper we analyse the techncal effcences of smallholder farms that operates n dfferent clean and safe vegetables farmng methods n northern Thaland. Specfcally, farmers were categorsed nto three groups based on ther use of synthetc chemcals: organc, pestcde-free and safe-use. Organc farmng refers to technologes wthout the use of chemcals or synthetc fertlsers durng producton or processng. Pestcde-free and safe-use practces on the other hand, are possble stages before convertng conventonal farms to organc farms. Techncal effcences of the three farmng systems were predcted usng stochastc fronter analyss. To mae productvty comparsons across farmng system, a metafronter approach was employed. The technology gaps were estmated on vegetable farms under dfferent technologes relatve to the potental technology avalable. Our objectves were to dentfy factors affectng producton n dfferent vegetable farmng systems and to evaluate farm performance. Ths paper s organsed as follows. Frst, we gve a bref overvew of clean and safe vegetable farmng systems n northern Thaland. Then ssues of productvty dfferences n clean and safe farmng are dscussed after whch we descrbe the analytcal framewor ncorporatng stochastc fronter analyss and metafronter analyss. Dscusson of results and ther mplcatons are presented n the next two sectons and we end wth some conclusons. 2. Clean and safe farmng systems n Thaland Clean and safe agrcultural systems n Thaland are manly developed for soco-economc and ecologcally sustanable development as well as to ncrease awareness of health and envronment hazard pressure. Although rce s the most mportant crop n Thaland base on consumpton, producton and ncome, vegetable crops are also sgnfcant as an alternatve source of household ncome and are consdered to be essental goods partcularly n the North. When food safety ssues are consdered, vegetables are subject to a hgh rs from chemcal contamnaton because of producton practces and consumpton behavour (Vant- Anuncha 2006; Posr et al. 2007). As a result, a project on safe chemcal use n the producton of vegetables was frst mplemented n Thaland n 1993. The development of organc farmng, whch was the only recognsed clean and safe agrculture at the tme, was drven manly by non-government organsatons (NGOs) to counterbalance the Green Revoluton. The systems frst appeared n home gardens and were expanded to commercal farms (Panyaul 2003). Clean and safe has ganed wder publc nterest to the publc snce 2001, after the Tha government mplemented a seres of polces related to clean and safe food and farmng. The most mportant Act was n 2004-2005 when the Government ran the Food Safety Year 2004 campagn to motvate publc awareness for 3
safety and food qualty (Salapetch 2007). Moreover, the Mnstry of Publc Health declared 2004 as the Year of Health for All and campagned for so-called clean and healthy food. In the followng year, organc agrculture was promoted as a natonal agenda coverng ssues such as food safety, sol and natural sources conservaton, and farmer awareness of consumers health. The agenda ams to reduce agrcultural chemcal use by 50 per cent and ncrease organc land up to 2.72 mllon ra (0.44 mllon hectares) by 2009. Snce May 2008, there has been progress on the natonal plan for organc agrculture promoted by the Natonal Organc Agrculture Development Board. The board s a collaboraton of government agences, prvate organsatons, NGOs and resource persons from farmer groups and academa. It ams to mprove the qualty of lfe of farmers and consumers, promote food securty, poverty reducton, and enhance capacty n organc producton and maretng. Four strateges were formulated to acheve the goals: focusng on consoldatng the nowledge base, capturng local nowledge, maretng mprovement and networng (Natonal Organc Agrculture Development Board 2008). Northern Thaland, partcularly Chang Ma Provnce, s consdered the most promsng and mportant vegetable producng regon wth ts dverse ecosystems and favourable growng condtons for tropcal and sub-tropcal crop speces (Gypmantasr et al. 2000). Clean and safe farmng systems n northern Thaland were ntally practced by smallholder farmers n ther home-gardens. After clean and safe agrcultural products became an mportant ssue among traders and government organsatons, the systems were also adopted commercally near bg ctes. Agrcultural converson to clean and safe producton systems, ncludng organc farmng practces n northern Thaland, are commonly encouraged by alternatve or sustanable agrculture ntatves of NGOs. Also, government polces have been developed for mplementaton by both publc and prvate organsatons (Kramol et al. 2009). Two types of clean and safe agrcultural producton systems are operatng, followng dfferent approaches. The frst type s based on self-suffcency, emphassng household food securty, food safety and ncome stablty (Panyaul 2003). Ths farmng and maretng system s often organsed through groups and networs, encouraged manly by NGOs, and by government organsatons and unverstes. Natural ecosystems wth a polyculture of local vegetables, herbs, medcal plants and frut crops are mplemented n a home-garden system. Outputs are mostly sold n alternatve marets such as farmers marets and specal retal outlets whch sell green and healthy products. Only a few smallholder farmers dstrbute ther organc produce to supermarets and hypermarets. The second lne of producton s focused more on the maret-drven organc producton system and s mostly engaged by prvate exporters and organsatons such as the Royal Project Foundaton (Kramol et al. 2005). The produce s manly dstrbuted to supermarets and hypermarets and exported to overseas marets. Clean and safe agrculture n northern Thaland s n varous stages of converson from the heavly dependent use of chemcals to no use of chemcals. The range of 'clean and safe' vegetable farmng practces n Thaland can be seen as a clean contnuum (McCoy and Parlevlet 2000) (Fgure 1). The clean contnuum ranges from producton practces that allow the use of hgh chemcal nputs, safe-chemcal, pestcde-free to no chemcal use wth envronmental frendly practces (organc). The deal of the clean and safe produce system s the organc method that allows the use of organc substtutes such as alternatve fertlsers and herbal pestcdes rather than synthetc chemcals. Safe-use and pestcde-free farmng are ntermedate practces between the organc and conventonal farmng. A pestcde-free farmng system s consdered a step taen before organc practce snce ts systems tend to 4
have smlar concepts to organc farmng systems. However, the pestcde-free method allows the use of synthetc fertlsers to mprove farmers ablty to enhance vegetable yelds. The safe-use farmng system allows the use of synthetc or artfcal chemcal fertlsers, nsectcdes, fungcdes and herbcdes provded the practces strctly follow the system s gudelnes. Produce from ths system s normally tested for safe levels of chemcal resdues. Ths farmng practce follows the Good Agrcultural Practce developed by the Departments of Agrculture and Agrcultural Extenson as a producton gudelne (Salapetch 2007). Conventonal farms are manstream farmng practces that conform to the standard, domnant farmng approach (Krstansen et al. 2006). The four systems are also dfferent n terms of ther dversfcaton on farms, followng the clean contnuum as well. Organc farms are lely to have more vegetable speces than others, whle conventonal has the least vegetable speces on farms. Pestcde-free farmers tend to farm numerous cash vegetables based on maret needed. Whle safe-use farms are n between pestcde-free farms and conventonal farms n terms of farm dversfcaton and vegetable speces. Wld harvest CHEMICAL/INDUSTRIAL Bodynamc/ Organc/ Fuuoa/Sant Asoe Chemcal free/ Pestcde free Bologcal control/ipm BIOLOGICAL/ECOLOGICAL Low nput/ safe chemcal use/ GAP Conventonal Hgh chemcal nput Fgure 1 The range of clean and safe farmng systems n Thaland Source: Adapted from McCoy and Parlevlet (2000). Clean and safe and conventonal vegetables have dfferent farmng practces and maretng arrangements. Vegetable produce s dstrbuted through wholesale traders. Organc vegetables are commonly dstrbuted to specal marets through maretng arrangements supported by NGOs, government organsatons and prvate companes (Kramol et al. 2009). Maretng arrangements such as farmer marets, nsttutonal marets and fars are commonly found as maretng supports. Pestcde-free vegetables are commonly sold n the same marets as organc produce, whle safe-use vegetables are manly sold through conventonal marets and partly dstrbuted through farmer marets, nsttutonal marets and fars trade. Conventonal vegetables are generally dstrbuted through traders to wholesale marets. 3. Productvty dfferences n clean and safe vegetable farmng Although vegetable producton s only 0.7 per cent of GDP (NESDB 2004), vegetable crops have a hgher potental margnal return per area than rce and fbre crops (Isvlanonda 1992). There s sparse lterature on the productvty and effcency of clean and safe farmng systems, especally n Thaland. Tha studes on clean and safe farmng productvty are concentrated on rce crops, comparng the proftablty and performance of organc and conventonal rce farmng. Studes on effcency n organc farmng n other countres have focused on lvestoc, dary 5
and mxed crop farms, partcularly n Europe. Prevous studes compared organc and conventonal farms usng separate producton fronters usng data envelopment analyss (DEA) and stochastc fronter analyss. Oude Lansn et al. (2002) compared the effcency and productvty of conventonal and organc farms n Fnland usng DEA. The study covered crop farms and lvestoc farms and concluded that organc farms are more effcent relatve to ther own technology but use sgnfcantly less productve technology than conventonal farms. Kumbhaar et al. (2008) studed dary farmng n Fnland and found that organc dary farms were fve per cent less effcent than conventonal farms. In Italy, Madau (2005) compared organc cereal farm effcency wth the effcency of conventonal farms, and concluded that organc farms used ther varable resources less effectvely. The mean techncal effcency estmates for conventonal and organc practces were very close at 0.892 and 0.825, respectvely. However, tests dd not ndcate that conventonal farms were more productve than organc farms. 4. Analytcal framewor In ths study, we employed two estmaton procedures. Frst, stochastc fronter analyss was used to analyse the techncal effcency of farms that use dfferent producton technologes. Second, a metafronter approach was appled to dscern technology gaps between the groups of farms usng these dfferent producton technologes. 4.1 Stochastc fronter analyss Based on Agner, Lovell and Schmdt (1977), the stochastc fronter model s able to measure a composed error structure n producton functon estmaton. A two-sded symmetrc error captures the random effects that are beyond the control of the producer. A one-sded error component captures techncal neffcency. The stochastc producton fronter model can be represented as Y f X, ) exp( V U ) (1) ( where Y s the scalar output of the -th farm; X s a vector of N nputs used by producer ; f(x, β) s the producton fronter; and β s a vector of technology parameters whch need to be estmated. V s ntended to capture the effect of statstcal nose and U expresses the techncal neffcency n producton. 4.2 Metafronter analyss Frms n dfferent crcumstances (such as logstcs and systems) face dfferent producton opportuntes. In such cases, entrepreneurs mae choces from dfferent technology sets wth varyng sets of feasble nput and output combnatons. Technology sets dffer n terms of human captal, economc nfrastructure, resource endowments and socoeconomc envronment. As a result, fronters should be estmated separately for each technology set n order to measure the techncal effcency of the dfferent groups of frms. However, the comparson of effcency levels measured relatve to dfferent fronters s commonly unattanable because one fronter may not be comparable to another. The metafronter framewor was frst ntroduced by Hayam (1969) and Hayam and Ruttan (1970; 1971) and developed extensvely by Battese and Rao (2002), Battese et al. (2004) and O'Donnell et al. (2008). The framewor allows the comparson of techncal neffcences across a number of frms n an ndustry whch have dfferent technologes. Measurement of the technology gap s undertaen n order to mae ths comparson. The boundary of an unrestrcted technology set s defned as a common metafronter, whle the boundares of restrcted technology sets are defned as group fronters. 6
As the metafronter envelops the group fronters, effcences measured relatve to the metafronter can be dvded nto two components. The frst component s assocated wth the common measure of techncal effcency that measures the dstance from an nput-output relatve to the group fronters. The other component measures the dstance between the group fronters and the metafronter, whch corresponds to the restrctve characterstcs of the producton technologes. Followng Battese and Rao (2002), equaton (1) can be re-expressed as a stochastc fronter of each group- fronter model as: Y f ( X, )exp( V U ) Y f ( X, ) e V U X V U e (2) where X s the nputs quantty of the -th frm; s an unnown parameter vector assocated wth the -th group; V represents statstcal nose and s assumed to be 2 ndependently and dentcally dstrbuted as N(0, ) random varables; and U represent 2 neffcency defned by the truncaton (at zero) of the N(, ) dstrbutons, where U s are defned by an approprate neffcency model followng Battese and Coell (1995). The techncal effcency of the -th frm wth respect to the group- fronter can be obtaned usng: U TE e (3) X V e Y A stochastc metafronter producton functon model n all frms can be expressed as: Y * * X f ( X, ) e (4) * where the constrants: * Y s the metafronter output and X * X V U * s the vector of metafronter parameters satsfyng for all = 1,2,,K (5) The constrants gven by equaton (5) mply that the metafronter functon cannot fall below any of the group fronters. Followng O'Donnell et al. (2008), an estmated metafronter functon whch envelops the estmated group fronters can be obtaned by applyng the optmsaton problem. Equaton (2) can be alternatvely expressed n terms of the metafronter functon n equaton (5) as Y e U e. e X X * X V *. e (6) U where s defned by equaton (3), the techncal effcency of the -th frm wth respect to the group fronter. The second term represents the technology gap rato (TGR) or metatechnology rato (MTR). 7 e
TGR x e or MTR * where 0 TGR 1 (7) x e The TGR measures the rato of the output n the fronter producton functon for the -th group relatve to the potental output defned by the metafronter functon. As the value of TGR approaches 1, the gap between the group fronter and the metafronter decreases. The techncal effcency of the -th frm relatve to the metafronter s denoted by TEm and s defned n a smlar way to equaton (3). It s the rato of the observed output relatve to the metafronter output the last term on the rght-hand sde of equaton (6). Techncal effcency s then defned as: TEm X * V e Y The techncal effcency relatve to the metafronter s the rato of the observed output to the fronter output. Then, where TEm TE TGR (9) TE and 4.3 Emprcal model TGR are predctors dscussed n connecton to equaton (3). In ths study, clean and safe vegetable farmng systems are consdered to be dfferent, at least n terms of ther sets of technology needs. Dfferent technologes mght have dfferent producton performances and effcences. Stochastc fronter analyss and metafronter analyss, mentoned n the prevous dscusson, were used to estmate techncal effcency and the TGRs. The frst one s used to analyse Output techncal effcency n each farmng system Metafronter Group4 and the second s used to compare producton fronters among farmng systems. A stochastc fronter producton functon s appled to cross-secton data n four models of vegetable farmng systems n northern Thaland. Vegetable farms n ths study are categorsed nto four farmng systems: three clean and safe and conventonal. Wth dfferent technology sets among those four groups (three clean and safe and conventonal farmng systems), the metafronter approach s an approprate method n order to mae a comparson of the four systems. The expresson of group fronters and metafronter s llustrated n Fgure 4. The specfcaton of the translog functonal form s gven by Group1 Group2 5 5 5 1 ln Y ln X X X D V U j j ln ln (10) 0 2 js j s j1 j1 s1 where j represents the j-th nput (j=1,2,,5) of the -th frm (1,2,,N ); j = j for all j and ; 1 = organc vegetable (OV) farmng method, 2 = pestcde-free vegetable (OV) farmng 8 Input Group3 Fgure 4 Metafronter functon model Source: Battese et al. (2004) (8)
method, 3 = safe- use vegetable (SUV) farmng method and 4 = conventonal vegetable (CV) farmng method; Y represents vegetable producton (baht); X 1 s the total area planted to vegetables (ra); X 2 s seed used (baht); X 3 s labour used (man-days); X 4 s fertlsers used (baht); X 5 s crop protecton used (baht); D 1 and D 2 are dummy varables for zero values of X 4 and X 5, respectvely; D 3 s a dummy varable for farms that used only synthetc fertlser and pestcde; and D 4() s a locaton dummy varable of farms areas wth alttude at least 750 meters. All varables except the dummes are mean-corrected to zero, hence, the frst-order estmates of the coeffcents n the model are nterpreted as elastcty. Followng the techncal neffcency model specfcaton of Battese and Coell (1995), we have: 6 j1 6 Z D (11) 0 j j s1 s s where js (j=0,1,..11) are unnown parameters; Z 1 s vegetable farmng experence (years); Z 2 s land holdng (ra); Z 3 s hghest educaton of members worng on farm (years); Z 4 s the average age of members worng on farm; Z 5 s off-farm ncome (baht); Z 6 s the proporton of famly labour worng on farm; D 4 s a locaton dummy (1, f hgh land (> 750mm), 0 otherwse); D 5 s a dummy varable for nformaton sources (1 f famers get agrcultural nformaton from mass communcaton, 0 otherwse); D 6 s a dummy varable for roles and poltcal poston (1 f farmers have any publc roles or poltcal poston, 0 otherwse); D 7 s a dummy varable for famng system (1 f mxed vegetable farm operated n a sustanable way (ncludng local vegetables), 0 otherwse); D 8 s a dummy varable for assstance provder (1 f an assstance provder s a government organsaton, 0 otherwse); D 9 s a dummy varable for assstance provder (1 f assstance provder s an NGO, 0 otherwse); D 10 s a dummy varable for assstance provder (1 f assstance provder s a prvate organsaton, 0 otherwse) and D 11 s a dummy for the number of vegetables grown on farms (1 f the farmer grows more than three vegetables, 0 otherwse). 4.4 Data and varables The data used n ths study were collected from a cross-secton sample of vegetable farms n northern Thaland, focusng on Chang Ma provnce n the crop year 2007/2008. An on-farm survey usng questonnares was appled to farmers on organc, pestcde-free, safe-use and conventonal farms. The respondent farms were 104 organc farms (OV), 88 pestcde-free farms (PFV), 88 safe-use farms (SUV) and 97 conventonal farms (CV). Descrptve statstcs of the varables ncluded n the stochastc fronter producton functons are summarsed n Table 2 and Table 3. The average vegetable farm ncome s about 118,350 baht, whch s approxmately 42,267 baht per ra. Table 2 shows dfferences n total ncome per vegetable farm across farmng systems where SUV and CV farms have about three tmes hgher ncome than OV and PFV farms. However, dfferences are qute small when scaled on a per area bass. Average vegetable farm area s about 2.8 ra, whch s about 0.45 hectare. SUV and CV farms have larger areas than OV and PFV farms. Seed use s about 5,521 baht per farm, wth PFV farms havng the lowest seed cost. The OV and PFV farms are labourntensve but have lower fertlser use and crop protecton cost than the SUV and CV farms. Locaton of vegetable farm s expected to nfluence vegetable producton snce there are envronmental dfferences such as temperature, sol condton and water sources. Only 36 per cent of farms were n locatons that have an alttude of at least 750 metres and most of them are OV and SUV farms. Lastly, more than half of SUV and CV farms used only synthetc 9
fertlser and pestcde. Table 2 Mean producton and neffcency varables by farmng system Varables Farmng system OV PFV SUV CV All Producton varables: Vegetable output (baht) 50,641 68,217 193,853 167,929 118,350 Vegetable output (baht/ra) 38,955 45,478 43,078 43,059 42,268 Area (ra) 1.3 1.5 4.5 3.9 2.8 Seed (baht) 3,509 2,240 7,576 8,791 5,521 Labour (man day) 285.6 343.5 474.2 358.4 361.9 Fertlser (baht) 2,808.9 5,063.7 31,108.3 28,801.6 16,628.7 Crop protecton (baht) 2,121.5 975.9 6,743.2 8,726.8 4,632.4 Ineffcency varables: Vegetable farm experence (years) 12.0 17.3 15.1 21.0 16.3 Land holdng (ra) 5.3 7.8 9.4 8.6 7.7 Hghest member educaton (years) 4.3 6.8 6.1 5.9 5.7 Age of members worng on farm 45.7 51.9 40.5 48.4 46.6 Off-farm ncome (1000 baht) 20.0 39.3 13.7 10.9 20.7 Proporton of famly labour (%) 94.5 96.0 85.5 86.2 90.6 Table 3 Percentage of factors ncluded n vegetable producton and neffcency model by farmng systems Varables Farmng system OV PFV SUV CV All Appled only synthetc chemcal 0 0 57 64 30 Locaton at alttude s hgher 750m 58 3 70 11 36 Informaton sources from mass 46 70 38 62 54 communcaton Roles and poltcal n vllage 24 38 19 11 23 Practses mxed vegetable farm n 29 45 1 0 19 sustanable way Government assstance 14 94 28 0 33 NGO assstance 27 0 0 0 7 Prvate assstance 58 0 69 0 32 Farm more than three vegetables 71 81 39 30 55 10
Summares of descrptve statstcs of neffcency varables are also provded n Table 2 and Table 3. The average age of members worng n the farms s about 46 years old. Farmers have vegetable farmng experence for 16 years on average. The PFV and CV farmers seem to have hgher age and more experences than others. The hghest educatonal attanment s commonly n prmary school, whch s sx schoolng years. The PFV farmers are the most educated group whle OV farmers are the least educated. OV and PFV farms had smaller land holdng than SUV and CV farms, and had a hgh proporton of household members worng on the farm. The latter also have less off-farm ncome. PFV households tended to receve a hgher proporton of mass communcaton nformaton, have publc roles and poltcal postons n the vllage, and practse vegetable farmng n sustanable ways. Growng few vegetables mproves the techncal effcency of CV and SUV farms n terms of smplfyng farm management and maretng actvtes. 5. Results and dscusson The maxmum-lelhood estmates of the parameters n the group fronters and neffcency models were estmated smultaneously. The values of explanatory varables n the translog stochastc fronter model were mean-corrected to zero; therefore the frst-order parameter estmates are partal output elastctes for the ndvdual nputs at ther mean values. Estmatons were obtaned usng the FRONTIER 4.1 program (Coell, 1996). 5.1 Stochastc producton fronter estmates For all group fronters, the techncal neffcency varables sgnfcantly add to the explanatory power of the model. Estmates of the stochastc fronter models are summarsed n Table 4. All nputs are found to be statstcally sgnfcant and have the expected postve effect on vegetable producton. Area and labour showed hghly sgnfcant effects on vegetable output for most farmng systems, whle seed s hghly sgnfcant for all groups except SUV farms. Fertlser s found to have hghly sgnfcant and postve mpact on SUV producton whle t s sgnfcant and postve at the 10 per cent level for OV farms. Crop protecton s sgnfcant only for OV farms at the 10 per cent sgnfcance level. The coeffcent for the dummy varable for farms usng only synthetc chemcals shows s negatve and sgnfcant at the 5 per cent level for CV farms. The coeffcent on the dummy varable for hghland farms s postve for all groups and sgnfcant at 1 per cent level except for OV farms where t s sgnfcant at the 5 per cent level. 5.2 Ineffcency effects Estmates of neffcency effects are presented n Table 5. They show that farmng systems are nfluenced by dfferent factors, and coeffcents have dfferent sgns among farmng systems. Vegetable farm experence and havng a government organsaton as an assstance provder have negatve and hghly sgnfcant effects on neffcency on SUV farms only. Havng NGOs has a negatve effect on neffcency for OV farms. The mean age of members worng on the farm and havng roles and postons n the vllage show sgnfcant and postve effects on neffcency for SUV farms only. Land holdng s found to have a sgnfcant and postve effect on neffcency for PFV farms but a negatve effect on neffcency for SUV farms. SUV and CV farm effcences mprove wth more educaton. Off-farm ncome s found to have a sgnfcant effect on neffcency wth negatve sgns for OV and SUV but wth a postve sgn for CV. Havng a hgher proporton of famly labour 11
worng on vegetable farm s found to lead to greater neffcency on PFV and SUV farms. Vegetable farms n hghland areas and followng sustanable practces show hgher effcency for PFV farms but lower effcency for SUV farms. Lastly, SUV and CV farms growng more than three vegetables are sgnfcantly have hgher neffcency. Table 4 Estmates of Parameters of the Translog Stochastc Fronter Models Varables Farmng Systems OV PFV SUV CV Pooled fronter Metafronter Constant 0.716 a 1.072 a -0.112 b 1.107 a 0.517 b 1.253 (0.0202) (0.141) (0.0579) (0.197) (0.278) Area 0.192 b 0.364 a 0.502 a 0.471 a 0.333 a 0.400 (0.0815) (0.0949) (0.0484) (0.0823) (0.0464) Seed 0.265 a 0.218 a -0.0411 0.203 a 0.116 a 0.153 (0.0529) (0.0798) (0.0392) (0.0577) (0.0314) Labour 0.546 a 0.469 a 0.328 a 0.205 b 0.458 a 0.339 (0.0889) (0.135) (0.0563) (0.0869) (0.0559) Fertlser 0.057 c 0.0388 0.392 a 0.0963 0.179 a 0.135 (0.0367) (0.0578) (0.0757) (0.0826) (0.0306) Crop protecton 0.0577 c -0.0137-0.00870 0.0595 0.0343 c 0.04 (0.04) (0.0616) (0.0318) (0.0622) (0.0249) Fertlser dummy -1.298 a -2.211 b -1.712 (0.114) (1.0217) Crop protecton dummy Synthetc chemcal dummy 0.0869-1.0936 0.0596-0.311 (0.771) (0.981) (0.487) 0.0550-0.188 b -0.036-0.075 (0.0856) (0.1) (0.0751) Hghland dummy 0.445 b 1.896 a 1.451 a 1.242 a 1.0548 1.217 (0.197) (0.523) (0.188) (0.506) (2.933) Sgma-squared 0.132 a 0.333 a 0.208 a 0.149 a 0.240 a (0.0193) (0.108) (0.0311) (0.0241) (0.0227) Gamma 0.999 a 0.999 a 0.999 a 0.999 a 0.438 c (0.000) (0.000) (0.000) (0.273) (0.273) Log lelhood functon LR test of onesde error -20.992-50.195-20.333-43.037-253.871 48.923 32.731 69.306 23.091 62.166 Note: Fgures n parentheses are standard errors a, b, and c denote sgnfcant usng a one-taled test at 1, 5, and 10 per cent levels, respectvely. 12
Table 5 Estmates of Parameters of the Ineffcency Effects Varables Farmng Systems OV PFV SUV CV Pooled fronter Constant 0.933 c -2.461 b -0.765 1.178 a 0.298 (0.647) (1.2) (0.747) (0.375) (0.522) Vegetable farm experence (years) 0.00656 0.00199-0.0331 a -0.00442-0.00124 (0.00569) (0.0128) (0.00795) (0.00438) (0.00302) Land holdng (ra) 0.00973 0.0254 c -0.0232 a 0.00539 0.00428 (0.0105) (0.0195) (0.00904) (0.00576) (0.00388) Hghest educaton of members worng on farm (years) 0.0154-0.00098-0.0289 c -0.048 b -0.00863 (0.0155) (0.0451) (0.0196) (0.0207) (0.00967) Average age of members worng on farm Off-farm ncome (1000 baht) Proporton of famly labour worng on vegetable farm Locaton at alttude at lease 750m Informaton sources from mass communcaton Roles and poltcal poston n vllage Practses mxed vegetable farm n sustanable way Assstance provder: government Assstance provder: non government Assstance provder: prvate Farm more than three vegetables 0.000309 0.0053 0.0124 c 0.00443-0.0019 (0.00609) (0.0215) (0.00742) (0.00804) (0.00437) -0.00208 b -0.00121-0.00405 c 0.00485 b -0.000335 (0.00103) (0.00314) (0.00277) (0.00225) (0.000717) 0.000297 0.0334 b 0.0167 a -0.000499 0.00523 b (0.00338) (0.0164) (0.00357) (0.00353) (0.00248) 0.388-2.818 a 1.428 a 1.165 b 0.697 (0.442) (1.115) (0.540) (0.523) (2.933) -0.0121-0.317-0.0711-0.082-0.0573 (0.0915) (0.374) (0.146) (0.113) (0.0644) 0.0219-0.14 0.902 a -0.0291-0.06 (0.128) (0.767) (0.225) (0.174) (0.085) -0.088-0.569 c 1.984 a -0.269 b (0.166) (0.431) (0.677) (0.122) -0.501 0.0736-1.816 a -0.0212 (0.395) (0.755) (0.593) (0.118) -1.345 a -0.93 (0.372) (0.743) -0.439 0.348 b (0.479) (0.151) 0.147 0.0736 0.621 a 0.258 b 0.259 a (0.129) (0.755) (0.136) (0.112) (0.0704) Note: Fgures n parentheses are standard errors a, b, and c denote sgnfcant usng a one-taled test at 1, 5, and 10 per cent levels, respectvely. 13
5.3 Metafronter estmates In order to test for dfferences n group fronters, the pooled stochastc fronter was estmated. We rejected the null hypothess of the generalsed lelhood rato test statstc that group fronters are the same. The generalsed lelhood rato test statstc that group fronter are the same s LR=223.5, wth a p-value of 0.000. Therefore, the estmaton of metafronter s justfed. The parameter estmates of the metafronter are presented n Table 4. The results show slghtly dfferent between the metafronter and the pooled fronter wth respect to the magntude of parameter. All nput varables have postve effect on mean vegetable producton. The mportant factors that strongly nfluence vegetable producton are area and labour (0.333-0.458). Seed and crop protecton show lower effects on vegetable producton wth an elastcty n the range 0.1-0.2. Crop protecton has low effects on producton on both pooled and metafronter (elastctes of 0.034 and 0.04). The estmates of techncal effcences and TGRs are presented n Table 6. The average techncal effcency estmates n all ndvdual farmng systems are relatvely low at 0.416, 0.474, 0.434 and 0.337 for OV, PFV, SUV and CV, respectvely. By loong at ndvdual farmng systems, the mean TE for PFV s hgher than the mean TEs for SUV, OV and CV. However, n order to compare effcences across farmng systems whch have dfferent technologes, technology gap ratos (TGRs) are taen nto account. On average, the estmated TGRs for OV, PFV, SUV and CV are 0.446, 0.631, 0.387 and 0.802 respectvely. The TGRs llustrate technology gap of vegetable farms n ndvdual vegetable farmng systems compared to all vegetable farms accordng to farm performance. The TGR of conventonal farms shows the hghest value meanng that conventonal farms have the least technology constrants than others. Average techncal effcences wth respect to the metafronter (TEm) are also provded n Table 6. They range from 0.149 to 0.286. Pestcde-free farms have the hghest TEm, followed by conventonal, OV and SV farms. The estmated densty functons of TE, TGRs and Tem are presented n Fgures 5 to 7. The dstrbutons of these ndcators are sgnfcantly dfferent from each other, as confrmed by statstcal tests conducted on the medan values. All farmng systems have a wde dstrbuton; OV and PFV have much more heterogeneous techncal effcency than others. Maxmum TGRs of one n every farmng system ndcate that farms n all systems are potentally able to remove technology constrants n order to mprove ther farm performance. 14
Table 6 Estmates of TE, TEm and TGR Model Item Farmng systems OV PFV SUV CV (N=104) (N=88) (N=88) (N=97) TE wth respect to the group fronter (TE) Mean 0.416 0.474 0.434 0.337 Medan* 0.293 0.411 0.278 0.320 Mn 0.099 0.042 0.043 0.054 Max 1.000 1.000 1.000 0.965 SD 0.276 0.241 0.326 0.178 Technology gap rato (TGR) Mean 0.446 0.631 0.387 0.802 Medan* 0.355 0.642 0.338 0.804 Mn 0.006 0.095 0.060 0.312 Max 1.000 1.000 1.000 1.000 SD 0.239 0.212 0.181 0.150 TE wth respect to the metafronter (TEm) Mean 0.220 0.286 0.149 0.266 Medan* 0.091 0.256 0.116 0.235 Mn 0.001 0.030 0.024 0.050 Max 0.995 0.944 0.597 0.794 SD 0.231 0.175 0.121 0.151 Note: * denotes sgnfcant for medan test at 1 per cent level usng Krusal-Walls test. 15
(a) OV (b) PFV (c) SUV (d) CV Fgure 5 Densty of farm-level techncal effcences wth respect to group fronter (a) OV (b) PFV (c) SUV (d) CV Fgure 6 Densty of technology gap ratos 16
(a) OV (b) PFV (c) SUV (d) CV Fgure 7 Densty of farm-level techncal effcences wth respect to the metafronter 6. Dscusson of results The results of estmates of farm performance based on techncal effcency wth respect to the specfc producton fronters dffer across farmng systems. These results provde an ndcator of the techncal effcency wth whch each of the farms was operatng wthn ther respectve technologcal group. All vegetable farmng groups have a low mean techncal effcency, from 0.337 for CV farms to 0.474 for PFV farms, and ndvdual techncal effcences that ranged wdely wthn each group. Ths suggests that a hgh proporton of vegetable farms n northern Thaland were not able to use ther nputs effectvely to acheve the hghest output possble, based on ther own technology sets. Clean and safe farms acheved a hgher TE score than conventonal farms, ndcatng a more effcent use of nputs n producng a certan level of output. It also suggests that the hgh nput use on seed, fertlser and pestcde dd not acheve hgh farm output on most conventonal farms. The TE values recorded here are lower than other studes (e.g. Madau 2005; Kumbhaar et al. 2008) presumably due the the more complex croppng patterns used n vegetable producton compared wth lvestoc and cereal producton. Techncal effcency wth respect to the metafronter mples that the technology gap ratos of clean and safe farmers are sgnfcantly lower than those of conventonal farmers where the mean TGR was 0.802. Hence, conventonal technology has a hgher producton capacty but lower effcency n transformng nputs nto outputs. For clean and safe farms, producton technology constrants were hghest on organc and safe-use farms, for whch mean TGRs were 0.446 and 0.387, respectvely. Technology constrants are lower on pestcde-free farms, where the mean TGR was 0.642. The fact that safe-use vegetable farms are found to have the lowest mean technology gap ratos s nterestng n that synthetc chemcal use s expected to mprove output capactes. Restrctve regulatons can be a major technology barrer snce safe-use farms, le organc farms, are requred to conform to strct 17
standard practces. The farms are nspected both on farmng practces and for the ssuance of produce qualfcatons and were expected to have the lowest mean TGR. Despte the low mean TGRs on clean and safe farms, all four farmng systems were found to have at least one farm that lay on the metafronter. Ths result means that at least some farmers practsng dfferent methods are able to elmnate technologcal constrants to acheve the hghest possble output regardless of the technology used. Famers abltes to operate vegetable farms effcently can be mproved by payng attenton to the effects of the neffcency varables. There are dfferent factors nfluencng neffcences n the four farmng methods. Assstance provded by NGOs s found to have large and postve mpacts on effcency levels on organc vegetable farms. As mentoned before, organc farms n northern Thaland follow two dfferent strateges: predomnantly self-suffcency and commercal farmng. The frst strategy, also called tradtonal or sustanable organc farmng, was ntally mplemented by NGOs. The system commonly has no use of any synthetc chemcals and farmers are encouraged to have the ablty to operate ther farms usng nternal nputs rather than purchase them from nput marets. Farmng assstance s commonly transferred through farmer groups and networs. The second strategy s appled on farms recevng advce and assstance from prvate organsatons. Here, converson to organc practces s n an early stage on these farms. Farmers operate ther farm dependng substantally on external nputs such as commercal alternatve pestcdes and fertlsers. Practcally, to acheve clean and safe practces organc farmers need relevant support on producton, postharvest and maretng practces, partcularly durng the converson perod (Kramol et al. 2005; Lorlowhaarn et al. 2008; Kramol et al. 2009). For these reasons, factors affectng the provson of farmer assstance need to be dentfed to mprove organc farm effcency. In addton, drect and practcal support from NGOs through partcpatory and communty-based tranng methods (Lorlowhaarn et al. 2008) should be consdered to enhance the process of technology transfer. On the contrary, producton neffcences of pestcde-free farms are lower than for organc farms, especally those located at an alttude of at least 750 metres. Pestcde-free farmers who mplement sustanable farmng concepts on ther farms have hgher farm effcences. The dea of sustanable farmng concept on pestcde-free farms s relevant to that of tradtonal organc farmng whch focuses on farm dversfcaton because the only obvous dsparty between organc and pestcde-free farms s that pestcde-free farmers are allowed the use of synthetc fertlsers. Safe-use and conventonal farms show farly smlar results on the factors affectng ther techncal neffcency. Hgher farmer educaton s found to reduce neffcency for farms n both groups. Ineffcency on conventonal farms could also be reduced by greater specalsaton n vegetable producton. Plantng fewer vegetable speces reduces tme and cost n producton and maretng. In addton, farms located at an alttude below 750 metres mprove effcency on conventonal farms because the farms are closer to marets (the central cty). On safe-use farms, results show that support from government organsatons can reduce techncal neffcency. Ths suggests that tranng farmers n producton slls and maretng arrangement enhances ther abltes to ncrease ther farm effcences. Low techncal neffcency on conventonal farms could be explaned by low or lac of producton and maretng support. The estmated techncal effcency and technology gap ratos n ths study could llustrate expected farm performances among vegetable farmng systems. Relatve to ther own technology, farms wth clean and safe practces commonly show hgher techncal effcency than conventonal farms. Ths mght be due to the support from a number of organsatons n 18
northern Thaland. The government could consder provdng greater assstance to conventonal farmers to help them mprove ther techncal effcency to levels acheved by farms wth clean and safe practces. On the other hand, technology gap ratos ndcate that clean and safe farmng methods have hgher producton constrants than conventonal farms. However, all farmng systems have at least one farmer located on the metafronter, mplyng that all practces are able to reduce constrants to acheve maxmum attanable productvty. In other words, the development of vegetable farms n terms of productvty s achevable for all farmng practces. In order to strengthen clean and safe farm productvty, nternal and external constrants on farmers need to be overcome through collaboratve assstance and support from relevant organsatons n both the publc and prvate sectors. The factors nclude: The effectveness and avalablty of producton technologes to deal wth agronomc constrants, especally crop protecton and nutrent management, s fundamental. The technology mprovement should be relevant to partcular farms crcumstances, condtons and logstcal systems. The technologes appled on the farm followng the drectons gven by dfferent assstance provders have ther own strengths and weanesses that should be consdered. The clean and safe ndustres need supply chan development to manage the maretng of farmers produce. Maretng nfrastructure (e.g. processng facltes, labellng schemes) and supply chans (for both farmers and maretng ntermedares) are mportant factors nfluencng farm effcency, proftablty and technology adopton. Improvement of farmer capacty n managng ther farms for both producton and maretng s needed. Farmers nowledge and perceptons about clean and safe agronomc practces and supply chan nvolvement are mportant factors for on-farm effcency, proftablty and technology adopton. Technology transfer processes should be consdered n order to mprove nnovaton and adopton. Clean and safe farmng technologes are complcated and have less economc beneft, partcularly n the converson perod. Although there are a number of assstance provders, agences and projects supportng farmers, mprovng the relevance and effectveness of ther technology transfer strateges s requred. 7. Conclusons Ths paper provdes a comparatve analyss of producton performance n three clean and safe vegetable famng systems and a conventonal vegetable famng system. The techncal effcences and technology gaps of organc, pestcde-free, safe-use and conventonal farms are analysed usng stochastc fronter analyss and metafronter analyss, respectvely. The techncal effcences wth respect to ndvdual fronters show that all group fronters have low mean techncal effcences. For conventonal farms, techncal effcences wth respect to ther fronter are lower than those for other groups. In contrast, the estmates of technology gap ratos for conventonal farms are sgnfcantly hgher than those for clean and safe farms. The group fronters of all farmng systems are found to touch the metafronter, meanng that all farmng systems have the potental to reduce ther technology constrants to acheve the hghest avalable productvty level. On ths bass, approprate support servces should enable clean and safe vegetable farms to reach productvty levels at least equvalent to those acheved by conventonal farms. 19
The wde range of techncal effcences and TGRs of farms n all farmng systems suggests that farm performance can be mproved by the provson of assstance servces. But the vast dfferences n producton performance among farmers ndcate that the extent and forms of assstance are lely to vary among members n each group. Organsatons and projects provdng assstance for clean and safe farmng methods that nfluence vegetable producton n northern Thaland stll have a lot of wor to do f farmers engagng n these practces are to acheve ther full potental. Varous factors, ncludng forms of support provded by dfferent organsatons, were found to mprove techncal effcency, but ther mpacts were not consstent across farmng systems. The neffcences of all ndvdual farmng systems are nfluenced by dfferent factors such as effectve assstance provders, farm locaton, farmer educaton and number of vegetable speces grown. Specfcally, mprovements are needed for agronomc technology, supply chans, farmer capacty n producton and maretng, and effectveness of technology transfer processes. References Agner, D., Lovell, C.A.K. and Schmdt, P. (1977). Formulaton and estmaton of stochastc fronter producton functon models, Journal of Econometrcs 6: 21-37. Battese, G.E. and Coell, T.J. (1995). A model for techncal neffcency effects n a stochastc fronter producton functon for panel data, Emprcal Economcs 20: 325-332. Battese, G.E. and Rao, D.S.P. (2002). Technology gap, effcency, and a stochastc metafronter functon, Internatonal Journal of Busness and Economcs 1: 1-7. Battese, G.E., Rao, D.S.P. and O'Donnell, C.J. (2004). A metafronter producton functon for estmaton of techncal effcences and technology gaps for frms operatng under dfferent technologes, Journal of Productvty Analyss 21: 91-103. Gypmantasr, P., Puangmanee, J., Thong-ngam, K., Chowslpa, N. and Lmnranul, B. (2000). Development Process of Pestcde-free Vegetable Producton Systems n Chang Ma Provnce. The Multple Croppng Centre, Chang Ma. Hayam, Y. (1969). Sources of agrcultural productvty gap among selected countres, Amercan Journal of Agrcultural Economcs 51: 564-575. Hayam, Y. and Ruttan, V.W. (1970). Agrcultural productvty dfferences among countres, Amercan Economc Revew 60: 895-911. Hayam, Y. and Ruttan, V.W. (1971). Agrcultural Development: An Internatonal Perspectve. Johns Hopns Unversty Press, Baltmore. Isvlanonda, S. (1992). Vegetables and fruts n Thaland: supply and demand, Paper submtted to Tha-EC Northeast Frut Vegetable Project Sectoral Economcs Program. Thaland Development Research Insttute. Johnson, G.I., Wenberger, K. and Wu, M.-H. (2008). The Vegetable Industry n Tropcal Asa: An Overvew of Producton and Trade, wth a Focus on Thaland. World Vegetable Center (AVRDC), Shanua, Tawan. Jurosze, P. and Tsa, H.-H. (2008). Research needs n organc vegetable producton systems n tropcal countres wth a focus on Asa., Cultvatng the Future Based on Scence: 2nd Conference of the Internatonal Socety of Organc Agrculture Research ISOFAR, Modena, Italy. Kramol, P., Thong-ngam, K., Gypmantasr, P. and Daves, W.P. (2005). Challenges n developng pestcde-free and organc vegetable marets and farmng systems for smallholder farmers n north Thaland. In Batt, P.J. and Jayamangala, N. (eds), Acta 20
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